1. Field of the Invention
The present invention relates to a system and method for teaching consumers about the interaction of credit data elements in determining a credit risk score. More particularly, the present invention relates to a system and method for simulating a consumer's credit risk score and for providing the consumer the opportunity to adjust his credit data to hypothetical values in order to observe the changes in the simulated score.
2. Description of Background Art
The practice of predicting a consumer's credit-worthiness is well known. Conventional credit-worthiness, or credit risk, analysis focuses on a consumer's credit history and other factors to determine whether credit should be offered or granted to the consumer.
Conventional credit risk analysis utilizes a risk model, or scorecard, to give a relative weight to each data element in the credit history to provide a credit-worthiness score. These models vary from provider to provider depending on the needs of the financial institution requesting the credit score. The methodology behind creating a risk model is known in the art.
Financial institutions that request a consumer's credit-worthiness score do so for several reasons. First, institutions often pre-screen potential applicants to determine to whom they should mail an offer of credit and at what terms. Typically, an institution will provide a score cut-off or tiered system for providing various terms and rates to different credit-worthiness score brackets. Pre-screening is used primarily to generate new business for the institution. Second, businesses use the credit-worthiness score in granting real-time requests by a consumer for a line of credit. This may include applying for a home mortgage, buying a car, or opening a new credit card account at the point of sale. In these instances, the credit score is requested and compared against the institution's credit risk policy to determine whether the new line of credit will be provided.
With creditors and lenders placing such a large emphasis on a consumer's credit-worthiness score, it is common for a consumer to want to improve his score. Typically, the scorecards utilize complex mathematical models and algorithms to arrive at a credit-worthiness score. The complexity of the scoring process is a stumbling block for consumers who want to understand how their scores were generated and how to improve their scores. Conventional attempts to educate consumers have failed to provide clear answers to consumers' questions. Most credit scoring vendors have deliberately withheld information on how credit scores are generated for fear of adding additional levels of confusion. Explanations and tutorials offered to consumers have been non-interactive and text-based. Furthermore, conventional solutions have not used the consumer's own credit data to illustrate the process.
Therefore there is a need for a system that (1) provides information to the consumer regarding score improvement, (2) uses the consumer's own credit data to illustrate the score generation process, and (3) allows the user to interact with the system and to experiment with different credit data in order to explore the hypothetical changes in his credit-worthiness score.